Automating Global Geospatial Data Set Analysis : Visualizing flood disasters in the cities of the Global South

Flooding is the most devastating natural hazard affecting tens of millions of people yearly and causing billions of USD dollars in damages globally. The people most affected by flooding globally are those with a high level of everyday vulnerability and limited resources for flood protection and recovery. Geospatial data from the Global South is severely lacking, and geospatial proficiency needs to be improved at a local level so that geospatial data and data analysis can be efficiently utilized in disaster risk reduction schemes and urban planning in the Global South. This thesis focuses on the use of automated global geospatial dataset analysis in disaster risk reduction in the Global South by using the Python programming language to produce an automated flood analysis and visualization model. In this study, the automated model was developed and tested in two, highly relevant cases: in the city of Bangkok, Thailand, and in the urban area of Tula de Allende, Mexico. The results of the thesis show that with minimal user interaction, the automated flood model ingests flood extent and depth data produced by ICEYE, a global population estimation raster produced by the German Aerospace Agency (DLR) and OpenStreetMap (OSM) data, performs multiple relevant analyses of these data, and produces an interactive map highlighting the severity and effects of a flooding event. The automated flood model performs consistently and accurately while producing key statistics and standardized visualizations of flooding events which offers first responders a very fast first estimation of the scale of a flooding event and helps plan an appropriate response anywhere around the globe. Global geospatial data sets are often created to examine large scale geographical phenomena; however, the results of this thesis show that they can also be used to analyze detailed local-level phenomena when paired together with supporting data. The advantage of using global geospatial data sets is that when sufficiently accurate and precise, they remove the most time-consuming part of geospatial analysis: finding suitable data. Fast reaction is of utmost importance in the first hours of a natural hazard like flooding, thus, automated analysis produced on a global scale could significantly help international humanitarian aid and first responders. Using an automated model also standardizes the results removing human errors and interpretation from the results enabling the accurate comparison of historical flood data in due time.Tulvat ovat luonnonilmiöihin liittyvistä riskeistä tuhoisimpia, ja ne vaikuttavat kymmeniin miljooniin ihmisiin vuosittain sekä aiheuttavat miljardien dollarien vahingot maailmanlaajuisesti. Tulvista kärsivät usein maailmanlaajuisesti ne ihmiset, jotka ovat jo ennestään haavoittuvia ja joilla on suhteellisesti heikoimmat keinot suojautua tulvilta ja selviytyä tulvan aiheuttamista tuhoista. Monissa globaalin etelän maissa on niukasti paikkatietoaineistoa ja paikkatieto-osaamista on syytä lisätä erityisesti paikallisella tasolla, jotta paikkatietoaineistoa ja analyysin hyödynnettävyyttä voidaan parantaa katastrofiriskien vähentämissuunnitelmissa sekä kaupunkisuunnittelussa globaalissa etelässä. Tämä opinnäytetyö keskittyy automatisoidun globaalin paikkatietoaineiston analyysin hyödyntämiseen katastrofiriskien vähentämisessä globaalissa etelässä käyttämällä Python-ohjelmointikieltä automatisoidun tulva-analyysi- ja visualisointimallin tuottamiseen. Tässä tutkimuksessa automatisoitua mallia kehitettiin ja testattiin kahdessa tulvariskien kannalta erittäin relevantissa tapauksessa: Bangkokissa, Thaimaassa ja Tula de Allende:n kaupunkialueella, Meksikossa. Tämän tutkielman tulokset osoittavat, että automatisoitu tulvamalli osaa lukea ICEYE:n tuottaman tulvan laajuus- ja syvyysaineiston, Saksan ilmailu- ja avaruuskeskuksen (DLR) tuottaman maailmanlaajuisen väestönarviorasterin, sekä OpenStreetMap (OSM) -aineiston, suorittaa aineistolle tulvan tuhojen tulkinnan kannalta olennaisia analyyseja, ja tuottaa lopputuloksena interaktiivisen kartan, joka korostaa tulvatapahtuman laajuutta ja vaikutuksia. Automatisoitu tulvamalli toimii johdonmukaisesti ja tuottaa tilastoja sekä standardoituja visualisointeja tulvatapahtumista, mikä tarjoaa ensivastehenkilöille erittäin nopean ensimmäisen arvion tulvatapahtuman laajuudesta. Tämä auttaa kohdentamaan pelastustoimenpiteitä riskitilanteessa vaihtelevissa ympäristöissä eri puolilla maailmaa. Globaalit paikkatietoaineistot luodaan usein laajojen maantieteellisten ilmiöiden tutkimiseen, mutta tämän tutkielman tulokset osoittavat kuitenkin, että niillä voidaan analysoida myös hyvin paikallistason ilmiöitä, kun ne yhdistetään muihin relevantteihin tietolähteisiin. Globaalien paikkatietoaineistojen käytön etuna on, että ollessaan riittävän tarkkoja ne poistavat paikkatietoanalyysin aikaa vievimmän osan: sopivan tiedon löytämisen. Nopea reagointi on äärimmäisen tärkeää luonnonuhkien, kuten tulvien, ensimmäisinä tunteina ja kansainvälisen humanitaarisen avun ja ensivastetoimijoiden tulisi hyödyntää maailmanlaajuisia automatisoituja analyysejä. Automaattinen malli myös standardoi tulokset poistaen tuloksista inhimilliset virheet ja tulkinnat, mikä mahdollistaa historiallisten tulvatietojen tarkan vertailun

Evaluation of Land Use Land Cover Changes in Nan Province, Thailand, Using Multi-Sensor Satellite Data and Google Earth Engine

Land use and land cover (LULC) conversion has become a chronic problem in Nan province. The primary factors of changes are lacking arable land, agricultural practices, and agriculture expansion. This study evaluated the usefulness of multi-sensor Landsat-5 (LS5), Landsat-8 (LS8), Sentinel-1 (S1), and Sentinel-2 (S2) satellite data for monitoring changes in LULC in Nan province, Thailand during a 30-year period (1990-2019), using a random forest (RF) model and the cloud-based Google Earth Engine (GEE) platform. Information of established land management policies was also used to describe the LULC changes. The median composite of the input variables selection from multi-sensor data were used to generate datasets. A total of 36 datasets showed the overall accuracy (OA) ranged from 51.70% to 96.95%. Sentinel-2 satellite images combined with the Modified Soil-Adjusted Vegetation Index (MSAVI) and topographic variables provided the highest OA (96.95%). Combination of optical (i.e., S2 and LS8) and S1 Synthetic Aperture Radar (SAR) data expressed better classification accuracy than individual S1 data. Forest cover decreased continuously during five consecutive periods. Coverage of maize and Pará rubber trees rapidly expanded in 2010-2014. These changes indicate an adverse consequence of the established economic development promoted by industrial and export agriculture. The findings strongly support the use of the RF technique, GEE platform and multi-sensor satellite data to enhance LULC classification accuracy in mountainous area. This study recommended that certain informative and science-based evidence will encourage local policymakers to identify priority areas for land management and natural resource conservation

Review on Active and Passive Remote Sensing Techniques for Road Extraction

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe

Local air quality management and health impacts of air pollution in Thailand

Air quality in urban areas of Chiang Mai Province, Thailand has seriously deteriorated as a consequence of population growth and urbanization and due to a lack of effective air quality management (AQM). As a result, respiratory diseases among Chiang Mai residents have increased in these affected areas. The health status and experiences of air pollution of both children and adults in Chiang Mai was assessed and improvements recommended to the developing AQM scheme. Air quality modelling, using ADMS-Urban was used to identify probable air polluted and control sites for further study. The polluted sites were found to be located along major roads in the city. However, ADMS-Urban was unable to predict air pollutant concentrations accurately because it could not cope with the very low wind speeds and complex topography of Chiang Mai. As a result, the utility of other air pollution modelling programmes should be investigated. The results of a questionnaire survey conducted with adults showed that urban respondents had a higher percentage of respiratory diseases than suburban respondents. However, later investigations were unable to establish a statistical linkage between air pollution concentrations and respiratory diseases. An ISAAC study was conducted among children attending schools located in the selected sites to assess the potential impacts of air pollution on health. The results showed that the prevalence of asthma was similar in all of the schools (approximately 5%) but that the prevalence of rhinitis (24.3% vs. 15.7%) and atopic dermatitis (12.5% vs. 7.2%) was higher in the urban schools which were considered to be more polluted. Logistic regression analysis identified other factors which may be involved in addition to pollution, including some components of the diet and contact with animals. In order to investigate the adequacy of the AQM system in Thailand, a comparative study was conducted between Hong Kong and Thailand. Both countries were investigated with respect to conformance to Good Urban Governance. The comparison showed that there are significant differences between the two countries and the AQM system in Hong Kong was more highly developed. For example, in contrast to the system in Hong Kong, it was found that there was insufficient involvement of the population in the development and implementation of AQM systems in Thailand. In order to better understand the reasons why the AQM system in Thailand is poor at both the provincial and local levels in Chiang Mai, prioritisation of AQM was assessed for major national environmental policies and plans; at the provincial level, fund allocations to development projects were reviewed; and at the sub-district level; a questionnaire survey was conducted among local government officials. It was concluded that AQM was not given sufficiently high priority in national plans and was generally ineffective and that, due to the non-specific nature of guidelines and frameworks in these plans, it was difficult for government organizations at the lower levels to establish AQM action plans for effective implementation. A range of appropriate measures to improve air quality in Chiang Mai were recommended. These included a more effective management of air pollution, an identified need for training and major changes in the transport system in the city

Kyoto University International ONLINE Symposium 2021 on Education and Research in Global Environmental Studies in Asia : Restarting International Cooperation After Covid-19 Pandemic

Kyoto University International ONLINE Symposium 2021 on Education and Research in Global Environmental Studies in Asia : Restarting International Cooperation After Covid-19 PandemicDate: NOV.29 (Mon.) to NOV.30 (Tue.), 2021Organized by: Kyoto University Graduate School of Global Environmental Studies (GSGES)Supported by: MEXT supporting project “Kyoto University Environmental Innovator Program–Cultivating Environmental Leaders across ASEAN Region”Study Field 1; Engineering・Technology・Science; E01-E40, except E02, E08, E17, E39Study Field 2; Agriculture・Forestry・Biology; A01-A20Study Field 3; Rural & Urban Development; R01-R16, E02, E08, E17, E39Study Field 4; Policy・Economics・Culture; P01-P1

Sizing and Positioning of Cylindrical Object Based on the Millimeter-Wave Radar System

In this article, we propose an algorithm to estimate the size and position of an object with the millimeter wave radar system. With the proposed algorithm, the size and position of objects is represented by cylinders with a single IWR1443 radar sensor module. From the estimated cylinder, the radius and center of the cross-sectional circle shows the approximate size and position. Raw data from IWR1443 show scanningof objects in the form of data points which cannot be directly used to determine the exact size or shape of the object. The data in one frame of the sensor provides many data points. With K-mean and selection of K, we can group the number of data points to each object and find the size and position by circle-fitting. The results of the algorithm show that the mean of a position of the object is close to the actual value, but the mean of the radius is rather smaller than the actual value

Semantic Labelling of Globally Distributed Urban and Non-Urban Satellite Images Using High Resolution SAR Data

While the analysis and understanding of multispectral (i.e., optical) remote sensing images has made considerable progress during the last decades, the automated analysis of SAR (Synthetic Aperture Radar) satellite images still needs some innovative techniques to support non-expert users in the handling and interpretation of these big and complex data. In this paper, we present a survey of existing multispectral and SAR land cover image datasets. To this end, we demonstrate how an advanced SAR image analysis system can be designed, implemented, and verified that is capable of generating semantically annotated classification results (e.g., maps) as well as local and regional statistical analytics such as graphical charts. The initial classification is made based on Gabor features and followed by class assignments (labelling). This is followed by the inclusion. This can be accomplished by the inclusion of expert knowledge via active learning with selected examples, and the extraction of additional knowledge from public databases to refine the classification results. Then, based on the generated semantics, we can create new topic models, find typical country-specific phenomena and distributions, visualize them interactively, and present significant examples including confusion matrices. This semi-automated and flexible methodology allows several annotation strategies, the inclusion of dedicated analytics procedures, and can generate broad as well as detailed semantic (multi-)labels for all continents, and statistics or models for selected countries and cities. Here, we employ knowledge graphs and exploit ontologies. These components could already be validated successfully. The proposed methodology can also be adapted to other instruments

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Robust and Flexible Persistent Scatterer Interferometry for Long-Term and Large-Scale Displacement Monitoring

Die Persistent Scatterer Interferometrie (PSI) ist eine Methode zur Überwachung von Verschiebungen der Erdoberfläche aus dem Weltraum. Sie basiert auf der Identifizierung und Analyse von stabilen Punktstreuern (sog. Persistent Scatterer, PS) durch die Anwendung von Ansätzen der Zeitreihenanalyse auf Stapel von SAR-Interferogrammen. PS Punkte dominieren die Rückstreuung der Auflösungszellen, in denen sie sich befinden, und werden durch geringfügige Dekorrelation charakterisiert. Verschiebungen solcher PS Punkte können mit einer potenziellen Submillimetergenauigkeit überwacht werden, wenn Störquellen effektiv minimiert werden. Im Laufe der Zeit hat sich die PSI in bestimmten Anwendungen zu einer operationellen Technologie entwickelt. Es gibt jedoch immer noch herausfordernde Anwendungen für die Methode. Physische Veränderungen der Landoberfläche und Änderungen in der Aufnahmegeometrie können dazu führen, dass PS Punkte im Laufe der Zeit erscheinen oder verschwinden. Die Anzahl der kontinuierlich kohärenten PS Punkte nimmt mit zunehmender Länge der Zeitreihen ab, während die Anzahl der TPS Punkte zunimmt, die nur während eines oder mehrerer getrennter Segmente der analysierten Zeitreihe kohärent sind. Daher ist es wünschenswert, die Analyse solcher TPS Punkte in die PSI zu integrieren, um ein flexibles PSI-System zu entwickeln, das in der Lage ist mit dynamischen Veränderungen der Landoberfläche umzugehen und somit ein kontinuierliches Verschiebungsmonitoring ermöglicht. Eine weitere Herausforderung der PSI besteht darin, großflächiges Monitoring in Regionen mit komplexen atmosphärischen Bedingungen durchzuführen. Letztere führen zu hoher Unsicherheit in den Verschiebungszeitreihen bei großen Abständen zur räumlichen Referenz. Diese Arbeit befasst sich mit Modifikationen und Erweiterungen, die auf der Grund lage eines bestehenden PSI-Algorithmus realisiert wurden, um einen robusten und flexiblen PSI-Ansatz zu entwickeln, der mit den oben genannten Herausforderungen umgehen kann. Als erster Hauptbeitrag wird eine Methode präsentiert, die TPS Punkte vollständig in die PSI integriert. In Evaluierungsstudien mit echten SAR Daten wird gezeigt, dass die Integration von TPS Punkten tatsächlich die Bewältigung dynamischer Veränderungen der Landoberfläche ermöglicht und mit zunehmender Zeitreihenlänge zunehmende Relevanz für PSI-basierte Beobachtungsnetzwerke hat. Der zweite Hauptbeitrag ist die Vorstellung einer Methode zur kovarianzbasierten Referenzintegration in großflächige PSI-Anwendungen zur Schätzung von räumlich korreliertem Rauschen. Die Methode basiert auf der Abtastung des Rauschens an Referenzpixeln mit bekannten Verschiebungszeitreihen und anschließender Interpolation auf die restlichen PS Pixel unter Berücksichtigung der räumlichen Statistik des Rauschens. Es wird in einer Simulationsstudie sowie einer Studie mit realen Daten gezeigt, dass die Methode überlegene Leistung im Vergleich zu alternativen Methoden zur Reduktion von räumlich korreliertem Rauschen in Interferogrammen mittels Referenzintegration zeigt. Die entwickelte PSI-Methode wird schließlich zur Untersuchung von Landsenkung im Vietnamesischen Teil des Mekong Deltas eingesetzt, das seit einigen Jahrzehnten von Landsenkung und verschiedenen anderen Umweltproblemen betroffen ist. Die geschätzten Landsenkungsraten zeigen eine hohe Variabilität auf kurzen sowie großen räumlichen Skalen. Die höchsten Senkungsraten von bis zu 6 cm pro Jahr treten hauptsächlich in städtischen Gebieten auf. Es kann gezeigt werden, dass der größte Teil der Landsenkung ihren Ursprung im oberflächennahen Untergrund hat. Die präsentierte Methode zur Reduzierung von räumlich korreliertem Rauschen verbessert die Ergebnisse signifikant, wenn eine angemessene räumliche Verteilung von Referenzgebieten verfügbar ist. In diesem Fall wird das Rauschen effektiv reduziert und unabhängige Ergebnisse von zwei Interferogrammstapeln, die aus unterschiedlichen Orbits aufgenommen wurden, zeigen große Übereinstimmung. Die Integration von TPS Punkten führt für die analysierte Zeitreihe von sechs Jahren zu einer deutlich größeren Anzahl an identifizierten TPS als PS Punkten im gesamten Untersuchungsgebiet und verbessert damit das Beobachtungsnetzwerk erheblich. Ein spezieller Anwendungsfall der TPS Integration wird vorgestellt, der auf der Clusterung von TPS Punkten basiert, die innerhalb der analysierten Zeitreihe erschienen, um neue Konstruktionen systematisch zu identifizieren und ihre anfängliche Bewegungszeitreihen zu analysieren